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Machine Learning is a science that enables machines (especially computers) to learn from environments and make own decisions.
Research Activities
- The Machine Learning Laboratory (MLL) carries out research and develops different theoretical foundations for machine learning such as:
- Reinforcement Learning, Deep Learning, Statistical Learning Theory, Multi-agent Systems, Game Theory and Mechanism Design, Blockchains, Explainable and Fair AI
- How machines and multi-agent systems should help in planning activities by learning from environments
- How learning gets affected if different machine learning and multi-agent systems algorithms are trying compete instead of cooperating
- How machines should learn in the presence of noisy environment or partial supervision
- Role of deep learning in planning, reinforcement learning, AI in game theory, blockchains and its applications
- For more details: Research Projects
Recent News
- MLL student Sankarshan Damle banged the prestigious Ripple-IIITH PhD fellowship for pursuing PhD in blockchain related areas.
- Two of our papers got accepted in ACML 2020
- Our paper ‘FNNC: Achieving Fairness through Neural Networks’ got accepted in IJCAI 2020
- Our lab members secure PhD admits with coveted research groups around the world: Susobhan Ghosh with the EconCS group at Harvard, Moin Moti with HKUST, and Rohith Gangam Reddy with UCI
- Three of our papers get accepted for full presentation in ALA, GAIW, EMAS, workshops at AAMAS 2020
Three of our papers get accepted for the main conference at AAMAS 2020 - Our Paper ‘Human-Machine Collaboration for Face Recognition’ got best paper runner up award at CoDS-COMAD 2020
- One paper is accepted in INFOCOM 2020
- Three of our papers get accepted in AAAI 2020